1. Field of the Invention
The present invention relates to apparatus for statistical model detection and more specifically to an apparatus for dynamically detecting a model from a series of input data.
2. Description of the Related Art
A number of dynamic model fitting methods have been proposed in the fields of statistics and data mining, a technique for the automated detection of hidden predictive information from databases to solve business decision problems. A paper titled “Information Theory and an Extension of the Maximum Likelihood Principle”, H. Akaike, in the Proceedings of the Second International Symposium on Information Theory, edited by B. N. Petrov and F. Csaki (Budapest: Akademia Kiado), pages 267-281, 1973 and a paper titled “Modeling By Shortest Data Description”, J. Rissanen, Automatica, Vol. 14, pages 465-471, 1978, describe a model fitting method for extracting an optimal model based on the regularity of time serial data. However, because of data regularity on which these prior art techniques are based, it is impossible to use the prior art for adaptively fitting a model on data sources that vary significantly with time.
Paper titled “Tracking the Best Expert”, M. Herbester and M. K. Warmuth, the Journal of Machine Learning, NN, 1-29 (1998), Kluwer Academic Publishers, Boston, pages 151-178, describes a successive prediction method for irregular time serial data in which the accumulated prediction loss is substantially equal to a value which would be obtained by the use of an optimal model at each successive point. While successive adaptive prediction can be used, the prior art is incapable of extracting an optimal model from the databases.
A Japanese-language technical paper titled “An Information-theoretic Approach to Detecting Anomalous Behaviors”, Yuko Matsunaga et al, the Forum of Information Technology (FIT), Information Technology Letters 2003, pages 123-124, describes a method of detecting anomalous behaviors by using dynamic model fitting based on predictive probabilistic complexity.
While this prior art provides adaptive and successive model detection, the level of precision that can be obtained from irregular time serial data is not satisfactory since this prior art extracts only one optimal model from all data that exist in the range from the beginning to the most recent time.